Supercomputers Improve Offshore Forecasting for the West Coast
Posted on 07 February 2020
In the ongoing race for improved weather systems, many research institutions continue to search for new and more powerful approaches for accurately predicting the weather. Now, a team from Oregon State University has used the supercomputing resources at the San Diego Supercomputer Center (SDSC) to develop an approach that could improve forecasts in the waters off the West Coast of the United States.
The research, which was published in Ocean Modelling in December, was conducted by Ivo Pasmans, a former doctoral student at Oregon State University, and Alexander Kurapov, an Oregon State professor. The duo set out to reduce forecasting errors in the three-day forecasts for water temperature, salinity levels, sea heights and currents off the coasts of Oregon and Washington. To do that, they used an approach called ensemble four-dimensional variational data assimilation – or En4DVAR – that allowed them to combine forecasts with observations.
To generate an ensemble that realistically represents the model error statistics, they have assumed that errors in the model’s wind forcing were the main source of model error, and they built on this concept using satellite wind observations and what’s known as a Bayesian Hierarchical Model to estimate a probability distribution for the wind errors.